Recently users of mobile devices such as flat panel computer, cellular phone increase dramatically, vehicle electronics and robotics are also developing rapidly. The speech applications of these areas may be seen growing in near future. Google's Nexus One and Motorola's Droid introduce active noise cancellation (ANC) technology to the mobile phone market, improve the input of speech applications, and make the back-end speech recognition or its application performing better, so that users may get better experience. In recent years, more mobile phone manufacturers are also actively involved in research of noise cancellation technology.
Common robust speech recognition technology includes two types. One type is the two-stage robust speech recognition technology, such kind of technology first enhances speech signal, and then transmits the enhanced signal to a speech recognition device for recognition. For example, uses two adaptive filters or combined algorithm of pre-trained speech and noise models to adjust an adaptive filter, enhances speech signal, and transmits the enhanced-signal to the speech recognition device. Another type uses a speech model as the basis for adaptive filter adjustment, but does not consider the information of noise interference. The criteria of this speech signal enhancement is based on maximum likelihood, that is, the better the enhanced speech signal more similar to the speech model.
FIG. 1 illustrates an exemplary schematic diagram of filter parameter adjustment process in a dual-microphone-based speech enhancement technology. The speech enhancement technology uses a re-recorded and filtered corpus to train a speech model 110, then uses the criterion of maximized similarity to adjust the noise filtering parameter y, that is, the criteria of the speech enhancement technique is determined by the better the enhanced speech signal 105a from the phase-difference-based time-frequency filtering 105 more similar to the speech model 110. The corpus for the training of the speech model 110 is needed to be re-recorded and filtered, and no noise information is considered, thus the setting for test and training conditions may be mismatched.
Dual microphone or microphone-array noise cancellation technology has a good anti-noise effect. However, in different usage environments, the ability of anti-noise is not the same. It is worth for research and development work on adjusting parameters of microphone array to increase speech recognition accuracy and provide better user experience.